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Advances in Human Biology: Combining Genetics and Molecular Biophysics to Pave the Way for Personalized Diagnostics and Medicine

DOI: 10.1155/2014/471836

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Abstract:

Advances in several biology-oriented initiatives such as genome sequencing and structural genomics, along with the progress made through traditional biological and biochemical research, have opened up a unique opportunity to better understand the molecular effects of human diseases. Human DNA can vary significantly from person to person and determines an individual’s physical characteristics and their susceptibility to diseases. Armed with an individual’s DNA sequence, researchers and physicians can check for defects known to be associated with certain diseases by utilizing various databases. However, for unclassified DNA mutations or in order to reveal molecular mechanism behind the effects, the mutations have to be mapped onto the corresponding networks and macromolecular structures and then analyzed to reveal their effect on the wild type properties of biological processes involved. Predicting the effect of DNA mutations on individual’s health is typically referred to as personalized or companion diagnostics. Furthermore, once the molecular mechanism of the mutations is revealed, the patient should be given drugs which are the most appropriate for the individual genome, referred to as pharmacogenomics. Altogether, the shift in focus in medicine towards more genomic-oriented practices is the foundation of personalized medicine. The progress made in these rapidly developing fields is outlined. 1. Introduction The human body is a delicate, self-regulating machine which can respond to its surroundings and internal needs. Such self-regulation involves various processes ranging from processes on atomic and molecular level to processes occurring in organs and tissues. Despite such tremendous complexity, somehow all humans, broadly speaking, are quite similar. However, slight differences in DNA can lead to a multitude of other physical differences. Some of these differences are harmless such as eye and hair color [1], race [2], and skin color [3, 4], while other differences may be disease-associated (see special J. Mol. Biol. issue [5]). The differences among individuals and their susceptibility to diseases are not only due to the single nucleoside polymorphisms (SNPs), but also due to the fact that different individuals have different copy numbers variations (CNVs) for various genes [6–9]. As pointed out by Haraksingh and Snyder [6], the CNVs are perhaps even more important for the humans than the SNPs, a statement supported by other researchers [10–13]. In the end, from the viewpoint of personalized diagnostics and medicine, the most important task is to

References

[1]  V. Kastelic and K. Drobni?, “A single-nucleotide polymorphism (SNP) multiplex system: the association of five SNPs with human eye and hair color in the Slovenian population and comparison using a Bayesian network and logistic regression model,” Croatian Medical Journal, vol. 53, no. 5, pp. 401–408, 2012.
[2]  T. J. Hoffmann, Y. Zhan, M. N. Kvale et al., “Design and coverage of high throughput genotyping arrays optimized for individuals of East Asian, African American, and Latino race/ethnicity using imputation and a novel hybrid SNP selection algorithm,” Genomics, vol. 98, no. 6, pp. 422–430, 2011.
[3]  J. M. de Gruijter, O. Lao, M. Vermeulen et al., “Contrasting signals of positive selection in genes involved in human skin-color variation from tests based on SNP scans and resequencing,” Investigative Genetics, vol. 2, no. 1, article 24, 2011.
[4]  S. Anno, T. Abe, and T. Yamamoto, “Interactions between SNP alleles at multiple loci contribute to skin color differences between caucasoid and mongoloid subjects,” International Journal of Biological Sciences, vol. 4, no. 2, pp. 81–86, 2008.
[5]  E. Alexov and M. Sternberg, “Understanding molecular effects of naturally occurring genetic differences,” Journal of Molecular Biology, vol. 425, no. 21, pp. 3911–3913, 2013.
[6]  R. R. Haraksingh and M. P. Snyder, “Impacts of variation in the human genome on gene regulation,” Journal of Molecular Biology, vol. 425, no. 21, pp. 3970–3977, 2013.
[7]  R. Chen, G. I. Mias, J. Li-Pook-Than et al., “Personal omics profiling reveals dynamic molecular and medical phenotypes,” Cell, vol. 148, no. 6, pp. 1293–1307, 2012.
[8]  H. Y. K. Lam, C. Pan, M. J. Clark et al., “Detecting and annotating genetic variations using the HugeSeq pipeline,” Nature Biotechnology, vol. 30, no. 3, pp. 226–229, 2012.
[9]  R. R. Haraksingh, A. Abyzov, M. Gerstein, A. E. Urban, and M. Snyder, “Genome-wide mapping of copy number variation in humans: comparative analysis of high resolution array platforms,” PLoS ONE, vol. 6, no. 11, Article ID e27859, 2011.
[10]  C. Genomes Project, G. R. Abecasis, A. Auton et al., “An integrated map of genetic variation from 1,092 human genomes,” Nature, vol. 491, pp. 56–65, 2012.
[11]  Genomes Project Consortium, G. R. Abecasis, D. Altshuler, et al., “A map of human genome variation from population-scale sequencing,” Nature, vol. 467, pp. 1061–1073, 2010.
[12]  D. F. Conrad, D. Pinto, R. Redon et al., “Origins and functional impact of copy number variation in the human genome,” Nature, vol. 464, no. 7289, pp. 704–712, 2010.
[13]  R. Redon, S. Ishikawa, K. R. Fitch et al., “Global variation in copy number in the human genome,” Nature, vol. 444, no. 7118, pp. 444–454, 2006.
[14]  C. Gonzaga-Jauregui, J. R. Lupski, and R. A. Gibbs, “Human genome sequencing in health and disease,” Annual Review of Medicine, vol. 63, pp. 35–61, 2012.
[15]  C. G. van El, M. C. Cornel, P. Borry, et al., “Whole-genome sequencing in health care: recommendations of the European society of human genetics,” European Journal of Human Genetics, vol. 21, supplement 1, pp. S1–S5, 2013.
[16]  C. E. Schwartz and C.-F. Chen, “Progress in detecting genetic alterations and their association with human disease,” Journal of Molecular Biology, vol. 425, no. 21, pp. 3914–3918, 2013.
[17]  O. R. Saram?ki, K. K. Waltering, and T. Visakorpi, “Methods for identifying and studying genetic alterations in hormone-dependent cancers.,” Methods in molecular biology, vol. 505, pp. 263–277, 2009.
[18]  N. Haiminen, D. N. Kuhn, L. Parida, and I. Rigoutsos, “Evaluation of methods for de novo genome assembly from high-throughput sequencing reads reveals dependencies that affect the quality of the results,” PLoS ONE, vol. 6, no. 9, Article ID e24182, 2011.
[19]  M. Scudellari, “The 24-hour, $1,000 genome,” Cancer Discovery, 2012.
[20]  L. deFrancesco, “Life technologies promises $1,000 genome,” Nature biotechnology, vol. 30, article 126, 2012.
[21]  E. R. Mardis, “The 1,000 genome, the 100,000 analysis?” Genome Medicine, vol. 2, no. 11, article 84, 2010.
[22]  J. Wise, “Consortium hopes to sequence genome of 1000 volunteers,” British Medical Journal, vol. 336, no. 7638, article 237, 2008.
[23]  B. M. Kuehn, “1000 genomes project promises closer look at variation in human genome,” The Journal of the American Medical Association, vol. 300, no. 23, article 2715, 2008.
[24]  M. Pybus, G. M. Dall'olio, P. Luisi, et al., “1000 genomes selection browser 1.0: a genome browser dedicated to signatures of natural selection in modern humans,” Nucleic Acids Research, 2013.
[25]  J. Amberger, C. A. Bocchini, A. F. Scott, and A. Hamosh, “McKusick's Online Mendelian Inheritance in Man (OMIM),” Nucleic Acids Research, vol. 37, no. 1, pp. D793–D796, 2009.
[26]  V. A. McKusick, “Mendelian Inheritance in Man and its online version, OMIM,” The American Journal of Human Genetics, vol. 80, no. 4, pp. 588–604, 2007.
[27]  H. J. W. Van Triest, D. Chen, X. Ji, S. Qi, and J. Li-Ling, “PhenOMIM: an OMIM-based secondary database purported for phenotypic comparison,” in Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '11), pp. 3589–3592, September 2011.
[28]  S. Rossi, A. Tsirigos, A. Amoroso et al., “OMiR: identification of associations between OMIM diseases and microRNAs,” Genomics, vol. 97, no. 2, pp. 71–76, 2011.
[29]  R. Cohen, A. Gefen, M. Elhadad, and O. S. Birk, “CSI-OMIM—clinical synopsis search in OMIM,” BMC Bioinformatics, vol. 12, p. 65, 2011.
[30]  C. D. Bajdik, B. Kuo, S. Rusaw, S. Jones, and A. Brooks-Wilson, “CGMIM: automated text-mining of Online Mendelian Inheritance in Man (OMIM) to identify genetically-associated cancers and candidate genes,” BMC Bioinformatics, vol. 6, article 78, 2005.
[31]  M. Bhagwat, “Searching NCBI’s dbSNP database,” in Current Protocols in Bioinformatics, chapter 1, unit 1.19, 2010.
[32]  S. F. Saccone, J. Quan, G. Mehta et al., “New tools and methods for direct programmatic access to the dbSNP relational database,” Nucleic Acids Research, vol. 39, no. 1, pp. D901–D907, 2011.
[33]  S. Teng, T. Madej, A. Panchenko, and E. Alexov, “Modeling effects of human single nucleotide polymorphisms on protein-protein interactions,” Biophysical Journal, vol. 96, no. 6, pp. 2178–2188, 2009.
[34]  Q. Cao, M. Zhou, X. Wang et al., “CaSNP: a database for interrogating copy number alterations of cancer genome from SNP array data,” Nucleic Acids Research, vol. 39, no. 1, pp. D968–D974, 2011.
[35]  G. Tuteja, E. Cheng, H. Papadakis, and G. Bejerano, “PESNPdb: a comprehensive database of SNPs studied in association with pre-eclampsia,” Placenta, vol. 33, no. 12, pp. 1055–1057, 2012.
[36]  J. Reumers, J. Schymkowitz, J. Ferkinghoff-Borg, F. Stricher, L. Serrano, and F. Rousseau, “SNPeffect: a database mapping molecular phenotypic effects of human non-synonymous coding SNPs,” Nucleic Acids Research, vol. 33, pp. D527–D532, 2005.
[37]  X. Liu, X. Jian, and E. Boerwinkle, “dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions,” Human Mutation, vol. 32, no. 8, pp. 894–899, 2011.
[38]  L. Guo, Y. Du, S. Chang, K. Zhang, and J. Wang, “rSNPBase: a database for curated regulatory SNPs,” Nucleic Acids Research, vol. 42, pp. D1033–D1039, 2014.
[39]  T. Zhang, Q. Zhou, Y. Pang et al., “CYP-nsSNP: a specialized database focused on effect of non-synonymous SNPs on function of CYPs,” Interdisciplinary Sciences: Computational Life Sciences, vol. 4, no. 2, pp. 83–89, 2012.
[40]  S. Bhushan and N. B. Perumal, “Disease associated cytokine SNPs database: an annotation and dissemination model,” Cytokine, vol. 57, no. 1, pp. 107–112, 2012.
[41]  International HapMap Consortium, “The International HapMap Project,” Nature, vol. 426, no. 6968, pp. 789–796, 2003.
[42]  T. R. Magalh?es, J. P. Casey, J. Conroy et al., “HGDP and HapMap analysis by Ancestry Mapper reveals local and global population relationships,” PLoS ONE, vol. 7, no. 11, Article ID e49438, 2012.
[43]  Y. J. Sung, C. C. Gu, H. K. Tiwari, D. K. Arnett, U. Broeckel, and D. C. Rao, “Genotype imputation for African Americans using data from HapMap phase II versus 1000 genomes projects,” Genetic Epidemiology, vol. 36, no. 5, pp. 508–516, 2012.
[44]  X. Gao, T. Haritunians, P. Marjoram et al., “Genotype imputation for Latinos using the HapMap and 1000 Genomes Project reference panels,” Frontiers in Genetics, vol. 3, article 117, 2012.
[45]  S. Garte, “Human population genetic diversity as a function of SNP type from HapMap data,” American Journal of Human Biology, vol. 22, no. 3, pp. 297–300, 2010.
[46]  C.-T. Liu, H. Lin, and H. Lin, “Functional analysis of HapMap SNPs,” Gene, vol. 511, no. 2, pp. 358–363, 2012.
[47]  A. K. Mitra, K. R. Crews, S. Pounds et al., “Genetic variants in cytosolic 5′-nucleotidase II are associated with its expression and cytarabine sensitivity in HapMap cell lines and in patients with acute myeloid leukemia,” Journal of Pharmacology and Experimental Therapeutics, vol. 339, no. 1, pp. 9–23, 2011.
[48]  X. Cao, A. K. Mitra, S. Pounds et al., “RRM1 and RRM2 pharmacogenetics: association with phenotypes in HapMap cell lines and acute myeloid leukemia patients,” Pharmacogenomics, vol. 14, no. 12, pp. 1449–1466, 2013.
[49]  T. Yamamura, J. Hikita, M. Bleakley et al., “HapMap SNP Scanner: an online program to mine SNPs responsible for cell phenotype,” Tissue Antigens, vol. 80, no. 2, pp. 119–125, 2012.
[50]  S. Stefl, H. Nishi, M. Petukh, A. R. Panchenko, and E. Alexov, “Molecular mechanisms of disease-causing missense mutations,” Journal of Molecular Biology, vol. 425, pp. 3919–3936, 2013.
[51]  Z. Zhang, M. A. Miteva, L. Wang, and E. Alexov, “Analyzing effects of naturally occurring missense mutations,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 805827, 2012.
[52]  S. Teng, E. Michonova-Alexova, and E. Alexov, “Approaches and resources for prediction of the effects of non-synonymous single nucleotide polymorphism on protein function and interactions,” Current Pharmaceutical Biotechnology, vol. 9, no. 2, pp. 123–133, 2008.
[53]  B. V. Halldorsson and R. Sharan, “Network-based interpretation of genomic variation data,” The Journal of Molecular Biology, vol. 425, pp. 3964–3969, 2013.
[54]  A. Califano, A. J. Butte, S. Friend, T. Ideker, and E. Schadt, “Leveraging models of cell regulation and GWAS data in integrative network-based association studies,” Nature Genetics, vol. 44, no. 8, pp. 841–847, 2012.
[55]  M. E. Smoot, K. Ono, J. Ruscheinski, P. Wang, and T. Ideker, “Cytoscape 2.8: new features for data integration and network visualization,” Bioinformatics, vol. 27, no. 3, Article ID btq675, pp. 431–432, 2011.
[56]  R. Saito, M. E. Smoot, K. Ono et al., “A travel guide to Cytoscape plugins,” Nature Methods, vol. 9, no. 11, pp. 1069–1076, 2012.
[57]  M. Smoot, K. Ono, T. Ideker, and S. Maere, “PiNGO: a cytoscape plugin to find candidate genes in biological networks,” Bioinformatics, vol. 27, no. 7, pp. 1030–1031, 2011.
[58]  M. S. Cline, M. Smoot, E. Cerami et al., “Integration of biological networks and gene expression data using Cytoscape.,” Nature Protocols, vol. 2, no. 10, pp. 2366–2382, 2007.
[59]  C. M. Tan, E. Y. Chen, R. Dannenfelser, N. R. Clark, and A. Ma'Ayan, “Network2Canvas: network visualization on a canvas with enrichment analysis,” Bioinformatics, vol. 29, no. 15, pp. 1872–1878, 2013.
[60]  S. Turkarslan, E. J. Wurtmann, W. J. Wu, et al., “Network portal: a database for storage, analysis and visualization of biological networks,” Nucleic Acids Research, vol. 42, pp. D184–D190, 2014.
[61]  W. Li, L. N. Kinch, and N. V. Grishin, “Pclust: protein network visualization highlighting experimental data,” Bioinformatics, vol. 29, no. 20, pp. 2647–2648, 2013.
[62]  D. Hurley, H. Araki, Y. Tamada et al., “Gene network inference and visualization tools for biologists: application to new human transcriptome datasets,” Nucleic Acids Research, vol. 40, no. 6, pp. 2377–2398, 2012.
[63]  P. Fariselli, O. Olmea, A. Valencia, and R. Casadio, “Progress in predicting inter-residue contacts of proteins with neural networks and correlated mutations,” Proteins: Structure, Function and Genetics, vol. 45, no. 5, pp. 157–162, 2001.
[64]  F. Pazos, M. Helmer-Citterich, G. Ausiello, and A. Valencia, “Correlated mutations contain information about protein-protein interaction,” Journal of Molecular Biology, vol. 271, no. 4, pp. 511–523, 1997.
[65]  H. Nishi, M. Tyagi, S. Teng et al., “Cancer missense mutations alter binding properties of proteins and their interaction networks,” PLoS ONE, vol. 8, no. 6, Article ID e66273, 2013.
[66]  K. Takano, D. Liu, P. Tarpey et al., “An x-linked channelopathy with cardiomegaly due to a CLIC2 mutation enhancing ryanodine receptor channel activity,” Human Molecular Genetics, vol. 21, no. 20, pp. 4497–4507, 2012.
[67]  T. K. B. Gandhi, J. Zhong, S. Mathivanan et al., “Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets,” Nature Genetics, vol. 38, no. 3, pp. 285–293, 2006.
[68]  A. Ghavidel, G. Cagney, and A. Emili, “A skeleton of the human protein interactome,” Cell, vol. 122, no. 6, pp. 830–832, 2005.
[69]  K. Rajapakse, D. Drobne, D. Kastelec, and R. Marinsek-Logar, “Experimental evidence of false-positive Comet test results due to TiO2 particle—assay interactions,” Nanotoxicology, vol. 7, no. 5, pp. 1043–1051, 2013.
[70]  T. N. Nguyen and J. A. Goodrich, “Protein-protein interaction assays: eliminating false positive interactions,” Nature Methods, vol. 3, no. 2, pp. 135–139, 2006.
[71]  S. Foerster, T. Kacprowski, V. M. Dhople et al., “Characterization of the EGFR interactome reveals associated protein complex networks and intracellular receptor dynamics,” Proteomics, vol. 13, pp. 3131–3144, 2013.
[72]  H. Bohnenberger, T. Oellerich, M. Engelke, H. H. Hsiao, H. Urlaub, and J. Wienands, “Complex phosphorylation dynamics control the composition of the Syk interactome in B cells,” European Journal of Immunology, vol. 41, no. 6, pp. 1550–1562, 2011.
[73]  E. Guney and B. Oliva, “Analysis of the robustness of network-based disease-gene prioritization methods reveals redundancy in the human interactome and functional diversity of disease-genes,” PLoS ONE, vol. 9, no. 4, Article ID e94686, 2014.
[74]  J. Love, F. Mancia, L. Shapiro et al., “The New York Consortium on Membrane Protein Structure (NYCOMPS): a high-throughput platform for structural genomics of integral membrane proteins,” Journal of Structural and Functional Genomics, vol. 11, no. 3, pp. 191–199, 2010.
[75]  R. Xiao, S. Anderson, J. Aramini et al., “The high-throughput protein sample production platform of the Northeast Structural Genomics Consortium,” Journal of Structural Biology, vol. 172, no. 1, pp. 21–33, 2010.
[76]  Z. Wunderlich, T. B. Acton, J. Liu, et al., “The protein target list of the northeast structural genomics consortium,” Proteins, vol. 56, no. 2, pp. 181–187, 2004.
[77]  A. R. Williamson, “Creating a structural genomics consortium,” Nature Structural Biology, vol. 7, p. 953, 2000.
[78]  E. Portugaly, I. Kifer, and M. Linial, “Selecting targets for structural determination by navigating in a graph of protein families,” Bioinformatics, vol. 18, no. 7, pp. 899–907, 2002.
[79]  P. W. Rose, C. Bi, W. F. Bluhm et al., “The RCSB protein data bank: new resources for research and education,” Nucleic Acids Research, vol. 41, no. 1, pp. D475–D482, 2013.
[80]  H. M. Berman, G. J. Kleywegt, H. Nakamura, and J. L. Markley, “Mini review: the future of the protein data bank,” Biopolymers, vol. 99, no. 3, pp. 218–222, 2013.
[81]  Y. Zhang, “I-TASSER server for protein 3D structure prediction,” BMC Bioinformatics, vol. 9, article 40, 2008.
[82]  D. M. Dunlavy, D. P. O'Leary, D. Klimov, and D. Thirumalai, “HOPE: a homotopy optimization method for protein structure prediction,” Journal of Computational Biology, vol. 12, no. 10, pp. 1275–1288, 2005.
[83]  D. Kihara, H. Lu, A. Kolinski, and J. Skolnick, “TOUCHSTONE: an ab initio protein structure prediction method that uses threading-based tertiary restraints,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 18, pp. 10125–10130, 2001.
[84]  S. D. Pickett, M. A. Saqi, and M. J. Sternberg, “Evaluation of the sequence template method for protein structure prediction: discrimination of the (beta/alpha)8-barrel fold,” Journal of Molecular Biology, vol. 228, no. 1, pp. 170–187, 1992.
[85]  W. Qu, H. Sui, B. Yang, and W. Qian, “Improving protein secondary structure prediction using a multi-modal BP method,” Computers in Biology and Medicine, vol. 41, no. 10, pp. 946–959, 2011.
[86]  Q. Cong, L. N. Kinch, J. Pei et al., “An automatic method for CASP9 free modeling structure prediction assessment,” Bioinformatics, vol. 27, no. 24, pp. 3371–3378, 2011.
[87]  D. Petrey, Z. Xiang, C. L. Tang et al., “Using multiple structure alignments, fast model building, and energetic analysis in fold recognition and homology modeling,” Proteins: Structure, Function and Genetics, vol. 53, supplement 6, pp. 430–435, 2003.
[88]  A. Kryshtafovych, K. Fidelis, and J. Moult, “CASP9 results compared to those of previous casp experiments,” Proteins: Structure, Function and Bioinformatics, vol. 82, supplement 2, pp. 164–174, 2014.
[89]  B. Stieglitz, L. F. Haire, I. Dikic, and K. Rittinger, “Structural analysis of SHARPIN, a subunit of a large multi-protein E3 ubiquitin ligase, reveals a novel dimerization function for the pleckstrin homology superfold,” Journal of Biological Chemistry, vol. 287, no. 25, pp. 20823–20829, 2012.
[90]  A. Silkov, Y. Yoon, H. Lee et al., “Genome-wide structural analysis reveals novel membrane binding properties of AP180 N-terminal homology (ANTH) domains,” The Journal of Biological Chemistry, vol. 286, no. 39, pp. 34155–34163, 2011.
[91]  P. Kundrotas, P. Georgieva, A. Shoshieva, P. Christova, and E. Alexova, “Assessing the quality of the homology-modeled 3D structures from electrostatic standpoint: test on bacterial nucleoside monophosphate kinase families,” Journal of Bioinformatics and Computational Biology, vol. 5, no. 3, pp. 693–715, 2007.
[92]  Z. Zhang, S. Witham, M. Petukh et al., “A rational free energy-based approach to understanding and targeting disease-causing missense mutations,” Journal of the American Medical Informatics Association, vol. 20, no. 4, pp. 643–651, 2013.
[93]  L. F. Agnati, A. O. Tarakanov, S. Ferré, K. Fuxe, and D. Guidolin, “Receptor-receptor interactions, receptor mosaics, and basic principles of molecular network organization: possible implications for drug development,” Journal of Molecular Neuroscience, vol. 26, no. 2-3, pp. 193–208, 2005.
[94]  J. R. Perkins, I. Diboun, B. H. Dessailly, J. G. Lees, and C. Orengo, “Transient protein-protein interactions: structural, functional, and network properties,” Structure, vol. 18, no. 10, pp. 1233–1243, 2010.
[95]  X. Kuang, J. G. Han, N. Zhao, B. Pang, C. Shyu, and D. Korkin, “DOMMINO: a database of macromolecular interactions,” Nucleic Acids Research, vol. 40, no. 1, pp. D501–D506, 2012.
[96]  A. A. Das, O. P. Sharma, M. S. Kumar, R. Krishna, and P. P. Mathur, “PepBind: a comprehensive database and computational tool for analysis of protein-peptide interactions,” Genomics, Proteomics & Bioinformatics, vol. 11, no. 4, pp. 241–246, 2013.
[97]  R. Rid, W. Strasser, D. Siegl et al., “PRIMOS: an integrated database of reassessed protein-protein interactions providing web-based access to in silico validation of experimentally derived data,” Assay and Drug Development Technologies, vol. 11, no. 5, pp. 333–346, 2013.
[98]  S. Kikugawa, K. Nishikata, K. Murakami et al., “PCDq: human protein complex database with quality index which summarizes different levels of evidences of protein complexes predicted from h-invitational protein-protein interactions integrative dataset.,” BMC Systems Biology, vol. 6, supplement 2, p. S7, 2012.
[99]  I. H. Moal and J. Fernández-Recio, “SKEMPI: a structural kinetic and energetic database of mutant protein interactions and its use in empirical models,” Bioinformatics, vol. 28, no. 20, pp. 2600–2607, 2012.
[100]  M. N. Wass, A. David, and M. J. Sternberg, “Challenges for the prediction of macromolecular interactions,” Current Opinion in Structural Biology, vol. 21, no. 3, pp. 382–390, 2011.
[101]  D. Baker, “Prediction and design of macromolecular structures and interactions,” Philosophical Transactions of the Royal Society B, vol. 361, pp. 459–463, 2006.
[102]  V. A. Roberts, M. E. Pique, L. F. Ten Eyck, and S. Li, “Predicting protein-DNA interactions by full search computational docking,” Proteins, vol. 81, pp. 2106–2118, 2013.
[103]  T. Clancy, E. A. R?dland, S. Nygard, and E. Hovig, “Predicting physical interactions between protein complexes,” Molecular and Cellular Proteomics, vol. 12, no. 6, pp. 1723–1734, 2013.
[104]  N. Fukuhara and T. Kawabata, “HOMCOS: a server to predict interacting protein pairs and interacting sites by homology modeling of complex structures,” Nucleic Acids Research, vol. 36, pp. W185–W189, 2008.
[105]  M. Takeda-Shitaka, G. Terashi, C. Chiba, D. Takaya, and H. Umeyama, “FAMS Complex: a fully automated homology modeling protein complex structures,” Medicinal Chemistry, vol. 2, no. 2, pp. 191–201, 2006.
[106]  P. J. Kundrotas, M. F. Lensink, and E. Alexov, “Homology-based modeling of 3D structures of protein-protein complexes using alignments of modified sequence profiles,” International Journal of Biological Macromolecules, vol. 43, no. 2, pp. 198–208, 2008.
[107]  P. Kundrotas and E. Alexov, “Predicting interacting and interfacial residues using continuous sequence segments,” International Journal of Biological Macromolecules, vol. 41, no. 5, pp. 615–623, 2007.
[108]  G. Launay and T. Simonson, “Homology modelling of protein-protein complexes: a simple method and its possibilities and limitations,” BMC Bioinformatics, vol. 9, article 427, 2008.
[109]  M. van Dijk and A. M. J. J. Bonvin, “Pushing the limits of what is achievable in protein—DNA docking: benchmarking HADDOCK’s performance,” Nucleic Acids Research, vol. 38, no. 17, Article ID gkq222, pp. 5634–5647, 2010.
[110]  P. Carter, V. I. Lesk, S. A. Islam, and M. J. E. Sternberg, “Protein-protein docking using 3D-Dock in rounds 3, 4, and 5 of CAPRI,” Proteins: Structure, Function and Genetics, vol. 60, no. 2, pp. 281–288, 2005.
[111]  D. Kozakov, R. Brenke, S. R. Comeau, and S. Vajda, “PIPER: an FFT-based protein docking program with pairwise potentials,” Proteins: Structure, Function and Genetics, vol. 65, no. 2, pp. 392–406, 2006.
[112]  S. Liang, G. Wang, and Y. Zhou, “Refining near-native protein-protein docking decoys by local resampling and energy minimization,” Proteins, vol. 76, no. 2, pp. 309–316, 2009.
[113]  M. F. Lensink and S. J. Wodak, “Docking, scoring, and affinity prediction in CAPRI,” Proteins, vol. 81, pp. 2082–2095, 2013.
[114]  M. F. Lensink, I. H. Moal, P. A. Bates, et al., “Blind prediction of interfacial water positions in CAPRI,” Proteins, vol. 82, no. 4, pp. 620–632, 2014.
[115]  M. F. Lensink and S. J. Wodak, “Blind predictions of protein interfaces by docking calculations in CAPRI,” Proteins: Structure, Function and Bioinformatics, vol. 78, no. 15, pp. 3085–3095, 2010.
[116]  M. F. Lensink and S. J. Wodak, “Docking and scoring protein interactions: CAPRI 2009,” Proteins: Structure, Function and Bioinformatics, vol. 78, no. 15, pp. 3073–3084, 2010.
[117]  D. Beglov, D. R. Hall, R. Brenke et al., “Minimal ensembles of side chain conformers for modeling protein-protein interactions,” Proteins: Structure, Function and Bioinformatics, vol. 80, no. 2, pp. 591–601, 2012.
[118]  Q. Wang, A. A. Canutescu, and R. L. Dunbrack Jr., “SCWRL and MolIDE: computer programs for side-chain conformation prediction and homology modeling,” Nature Protocols, vol. 3, no. 12, pp. 1832–1847, 2008.
[119]  M. J. Bower, F. E. Cohen, and R. L. Dunbrack Jr., “Prediction of protein side-chain rotamers from a backbone-dependent rotamer library: a new homology modeling tool,” Journal of Molecular Biology, vol. 267, no. 5, pp. 1268–1282, 1997.
[120]  Z. Xiang, P. J. Steinbach, M. P. Jacobson, R. A. Friesner, and B. Honig, “Prediction of side-chain conformations on protein surfaces,” Proteins: Structure, Function and Genetics, vol. 66, no. 4, pp. 814–823, 2007.
[121]  Z. Xiang and B. Honig, “Extending the accuracy limits of prediction for side-chain conformations,” Journal of Molecular Biology, vol. 311, no. 2, pp. 421–430, 2001.
[122]  S. Liang, C. Zhang, and Y. Zhou, “LEAP: highly accurate prediction of protein loop conformations by integrating coarse-grained sampling and optimized energy scores with all-atom refinement of backbone and side chains,” Journal of Computational Chemistry, vol. 35, no. 4, pp. 335–341, 2014.
[123]  K. Zhu and T. Day, “Ab initio structure prediction of the antibody hypervariable H3 loop,” Proteins: Structure, Function and Bioinformatics, vol. 81, no. 6, pp. 1081–1089, 2013.
[124]  S. Zhao, K. Zhu, J. Li, and R. A. Friesner, “Progress in super long loop prediction,” Proteins: Structure, Function and Bioinformatics, vol. 79, no. 10, pp. 2920–2935, 2011.
[125]  N. M. Glykos and M. Kokkinidis, “Meaningful refinement of polyalanine models using rigid-body simulated annealing: application to the structure determination of the A31P Rop mutant,” Acta Crystallographica Section D: Biological Crystallography, vol. 55, no. 7, pp. 1301–1308, 1999.
[126]  Z. Zhang, S. Teng, L. Wang, C. E. Schwartz, and E. Alexov, “Computational analysis of missense mutations causing Snyder-Robinson syndrome,” Human Mutation, vol. 31, no. 9, pp. 1043–1049, 2010.
[127]  N. Dolzhanskaya, M. A. Gonzalez, F. Sperziani, et al., “A novel p.Leu(381)Phe mutation in presenilin 1 is associated with very early onset and unusually fast progressing dementia as well as lysosomal inclusions typically seen in Kufs disease,” Journal of Alzheimer's Disease, vol. 39, no. 1, pp. 23–27, 2013.
[128]  L. Boccuto, K. Aoki, H. Flanagan-Steet, et al., “A mutation in a ganglioside biosynthetic enzyme, ST3GAL5, results in salt & pepper syndrome, a neurocutaneous disorder with altered glycolipid and glycoprotein glycosylation,” Human Molecular Genetics, vol. 23, no. 2, pp. 418–433, 2014.
[129]  C. M. Yates and M. J. E. Sternberg, “The effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on protein-protein interactions,” Journal of Molecular Biology, vol. 425, pp. 3949–3963, 2013.
[130]  M. Hecht, Y. Bromberg, and B. Rost, “News from the protein mutability landscape,” Journal of Molecular Biology, vol. 425, no. 21, pp. 3937–3948, 2013.
[131]  Z. Zhang, J. Norris, C. Schwartz, and E. Alexov, “In silico and in vitro investigations of the mutability of disease-causing missense mutation sites in spermine synthase,” PLoS ONE, vol. 6, no. 5, Article ID e20373, 2011.
[132]  L. Wickstrom, E. Gallicchio, and R. M. Levy, “The linear interaction energy method for the prediction of protein stability changes upon mutation,” Proteins: Structure, Function and Bioinformatics, vol. 80, no. 1, pp. 111–125, 2012.
[133]  Y. Li and J. Fang, “PROTS-RF: a robust model for predicting mutation-induced protein stability changes,” PLoS ONE, vol. 7, no. 10, Article ID e47247, 2012.
[134]  E. H. Kellogg, A. Leaver-Fay, and D. Baker, “Role of conformational sampling in computing mutation-induced changes in protein structure and stability,” Proteins: Structure, Function and Bioinformatics, vol. 79, no. 3, pp. 830–838, 2011.
[135]  Y. Dehouck, J. M. Kwasigroch, D. Gilis, and M. Rooman, “PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality,” BMC Bioinformatics, vol. 12, article 151, 2011.
[136]  C. M. Frenz, “Neural network-based prediction of mutation-induced protein stability changes in staphylococcal nuclease at 20 residue positions,” Proteins: Structure, Function and Genetics, vol. 59, no. 2, pp. 147–151, 2005.
[137]  E. Capriotti, P. Fariselli, and R. Casadio, “I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure,” Nucleic Acids Research, vol. 33, no. 2, pp. W306–W310, 2005.
[138]  G. Thiltgen and R. A. Goldstein, “Assessing predictors of changes in protein stability upon mutation using self-consistency,” PLoS ONE, vol. 7, no. 10, Article ID e46084, 2012.
[139]  S. Khan and M. Vihinen, “Performance of protein stability predictors,” Human Mutation, vol. 31, no. 6, pp. 675–684, 2010.
[140]  K. Schurmann, M. Anton, I. Ivanov, C. Richter, H. Kuhn, and M. Walther, “Molecular basis for the reduced catalytic activity of the naturally occurring T560m mutant of human 12/15-lipoxygenase that has been implicated in coronary artery disease,” Journal of Biological Chemistry, vol. 286, no. 27, pp. 23920–23927, 2011.
[141]  S. Wang, W. Zhao, H. Liu, H. Gong, and Y. Yan, “Increasing βB1-crystallin sensitivity to proteolysis caused by the congenital cataract-microcornea syndrome mutation S129R,” Biochimica et Biophysica Acta, vol. 1832, no. 2, pp. 302–311, 2013.
[142]  S. Witham, K. Takano, C. Schwartz, and E. Alexov, “A missense mutation in CLIC2 associated with intellectual disability is predicted by in silico modeling to affect protein stability and dynamics,” Proteins: Structure, Function and Bioinformatics, vol. 79, no. 8, pp. 2444–2454, 2011.
[143]  H. Tsukamoto and D. L. Farrens, “A constitutively activating mutation alters the dynamics and energetics of a key conformational change in a ligand-free G protein-coupled receptor,” The Journal of Biological Chemistry, vol. 288, pp. 28207–28216, 2013.
[144]  J. Y. Lee and D. S. Kim, “Dramatic effect of single-base mutation on the conformational dynamics of human telomeric G-quadruplex,” Nucleic Acids Research, vol. 37, no. 11, pp. 3625–3634, 2009.
[145]  R. Guerois, J. E. Nielsen, and L. Serrano, “Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations,” Journal of Molecular Biology, vol. 320, no. 2, pp. 369–387, 2002.
[146]  Y. Dehouck, J. M. Kwasigroch, M. Rooman, and D. Gilis, “BeAtMuSiC: prediction of changes in protein-protein binding affinity on mutations,” Nucleic Acids Research, vol. 41, pp. W333–W339, 2013.
[147]  A. Benedix, C. M. Becker, B. L. de Groot, A. Caflisch, and R. A. B?ckmann, “Predicting free energy changes using structural ensembles,” Nature Methods, vol. 6, no. 1, pp. 3–4, 2009.
[148]  T. Kortemme and D. Baker, “A simple physical model for binding energy hot spots in protein-protein complexes,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 22, pp. 14116–14121, 2002.
[149]  G. Rastelli, A. Del Rio, G. Degliesposti, and M. Sgobba, “Fast and accurate predictions of binding free energies using MM-PBSA and MM-GBSA,” Journal of Computational Chemistry, vol. 31, no. 4, pp. 797–810, 2010.
[150]  V. Z. Spassov and L. Yan, “pH-selective mutagenesis of protein-protein interfaces: in silico design of therapeutic antibodies with prolonged half-life,” Proteins: Structure, Function and Bioinformatics, vol. 81, no. 4, pp. 704–714, 2013.
[151]  R. Moretti, S. J. Fleishman, R. Agius, M. Torchala, and P. A. Bates, “Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions,” Proteins, vol. 81, pp. 1980–1987, 2013.
[152]  A. David, R. Razali, M. N. Wass, and M. J. E. Sternberg, “Protein-protein interaction sites are hot spots for disease-associated nonsynonymous SNPs,” Human Mutation, vol. 33, no. 2, pp. 359–363, 2012.
[153]  Y. Zhang, M. Motamed, J. Seemann, M. S. Brown, and J. L. Goldstein, “Point mutation in luminal Loop 7 of scap protein blocks interaction with Loop 1 and abolishes movement to Golgi,” The Journal of Biological Chemistry, vol. 288, no. 20, pp. 14059–14067, 2013.
[154]  B. A. Shoemaker, D. Zhang, M. Tyagi et al., “IBIS (Inferred Biomolecular Interaction Server) reports, predicts and integrates multiple types of conserved interactions for proteins,” Nucleic Acids Research, vol. 40, no. 1, pp. D834–D840, 2012.
[155]  E. W. Sayers, T. Barrett, D. A. Benson et al., “Database resources of the National Center for Biotechnology Information,” Nucleic Acids Research, vol. 40, no. 1, pp. D13–D25, 2012.
[156]  K. Talley and E. Alexov, “On the pH-optimum of activity and stability of proteins,” Proteins: Structure, Function and Bioinformatics, vol. 78, no. 12, pp. 2699–2706, 2010.
[157]  E. Alexov, “Numerical calculations of the pH of maximal protein stability: the effect of the sequence composition and three-dimensional structure,” European Journal of Biochemistry, vol. 271, no. 1, pp. 173–185, 2004.
[158]  P. Chan and J. Warwicker, “Evidence for the adaptation of protein pH-dependence to subcellular pH,” BMC Biology, vol. 7, article 69, 2009.
[159]  P. Chan, J. Lovri?, and J. Warwicker, “Subcellular pH and predicted pH-dependent features of proteins,” Proteomics, vol. 6, no. 12, pp. 3494–3501, 2006.
[160]  A. V. Onufriev and E. Alexov, “Protonation and pK changes in protein-ligand binding,” Quarterly Reviews of Biophysics, vol. 46, no. 2, pp. 181–209, 2013.
[161]  M. Kimura, J. Machida, S. Yamaguchi, A. Shibata, and T. Tatematsu, “Novel nonsense mutation in MSX1 in familial nonsyndromic oligodontia: subcellular localization and role of homeodomain/MH4,” European Journal of Oral Sciences, vol. 122, no. 1, pp. 15–20, 2014.
[162]  Y. Erzurumlu, F. Aydin Kose, O. Gozen, D. Gozuacik, E. A. Toth, and P. Ballar, “A unique IBMPFD-related P97/VCP mutation with differential binding pattern and subcellular localization,” International Journal of Biochemistry and Cell Biology, vol. 45, no. 4, pp. 773–782, 2013.
[163]  Y. Hosaka, H. Hanawa, T. Washizuka et al., “Function, subcellular localization and assembly of a novel mutation of KCNJ2 in Andersen's syndrome,” Journal of Molecular and Cellular Cardiology, vol. 35, no. 4, pp. 409–415, 2003.
[164]  P. J. Kundrotas and E. Alexov, “Electrostatic properties of protein-protein complexes,” Biophysical Journal, vol. 91, no. 5, pp. 1724–1736, 2006.
[165]  R. C. Mitra, Z. Zhang, and E. Alexov, “In silico modeling of pH-optimum of protein-protein binding,” Proteins: Structure, Function and Bioinformatics, vol. 79, no. 3, pp. 925–936, 2011.
[166]  M. Petukh, S. Stefl, and E. Alexov, “The role of protonation states in ligand-receptor recognition and binding,” Current Pharmaceutical Design, vol. 19, no. 23, pp. 4182–4190, 2013.
[167]  B. Aguilar, R. Anandakrishnan, J. Z. Ruscio, and A. V. Onufriev, “Statistics and physical origins of pK and ionization state changes upon protein-ligand binding,” Biophysical Journal, vol. 98, no. 5, pp. 872–880, 2010.
[168]  E. Alexov, E. L. Mehler, N. Baker et al., “Progress in the prediction of pKa values in proteins,” Proteins: Structure, Function and Bioinformatics, vol. 79, no. 12, pp. 3260–3275, 2011.
[169]  T. Carstensen, D. Farrell, Y. Huang, N. A. Baker, and J. E. Nielsen, “On the development of protein pKa calculation algorithms,” Proteins: Structure, Function and Bioinformatics, vol. 79, no. 12, pp. 3287–3298, 2011.
[170]  O. Emanuelsson, S. Brunak, G. von Heijne, and H. Nielsen, “Locating proteins in the cell using TargetP, SignalP and related tools,” Nature Protocols, vol. 2, no. 4, pp. 953–971, 2007.
[171]  A. H?glund, P. D?nnes, T. Blum, H. Adolph, and O. Kohlbacher, “MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition,” Bioinformatics, vol. 22, no. 10, pp. 1158–1165, 2006.
[172]  P. Horton, K. Park, T. Obayashi et al., “WoLF PSORT: protein localization predictor,” Nucleic Acids Research, vol. 35, no. 2, pp. W585–W587, 2007.
[173]  K. J. Won, X. Zhang, T. Wang et al., “Comparative annotation of functional regions in the human genome using epigenomic data,” Nucleic Acids Research, vol. 41, no. 8, pp. 4423–4432, 2013.
[174]  A. B. Munkacsi, A. F. Porto, and S. L. Sturley, “Niemann-Pick type C disease proteins: orphan transporters or membrane rheostats?” Future Lipidology, vol. 2, no. 3, pp. 357–367, 2007.
[175]  D. Avram, A. Fields, K. Pretty On Top, D. J. Nevrivy, J. E. Ishmael, and M. Leid, “Isolation of a novel family of C2H2 zinc finger proteins implicated in transcriptional repression mediated by chicken ovalbumin upstream promoter transcription factor (COUP-TF) orphan nuclear receptors,” The Journal of Biological Chemistry, vol. 275, no. 14, pp. 10315–10322, 2000.
[176]  J. Harrow, A. Frankish, J. M. Gonzalez et al., “GENCODE: the reference human genome annotation for the ENCODE project,” Genome Research, vol. 22, no. 9, pp. 1760–1774, 2012.
[177]  H. Chen, Y. Tian, W. Shu, X. Bo, and S. Wang, “Comprehensive identification and annotation of cell type-specific and ubiquitous CTCF-binding sites in the human genome,” PLoS ONE, vol. 7, Article ID e41374, 2012.
[178]  H. Jia, M. Osak, G. K. Bogu, L. W. Stanton, R. Johnson, and L. Lipovich, “Genome-wide computational identification and manual annotation of human long noncoding RNA genes,” RNA, vol. 16, no. 8, pp. 1478–1487, 2010.
[179]  R. Guigó, P. Flicek, J. F. Abril et al., “EGASP: the human ENCODE Genome Annotation Assessment Project,” Genome Biology, vol. 7, supplement 1, article S2, 31 pages, 2006.
[180]  P. Radivojac, W. T. Clark, T. R. Oron, et al., “A large-scale evaluation of computational protein function prediction,” Nature Methods, vol. 10, pp. 221–227, 2013.
[181]  J. Gillis and P. Pavlidis, “Characterizing the state of the art in the computational assignment of gene function: Lessons from the first critical assessment of functional annotation (CAFA),” BMC Bioinformatics, vol. 14, no. 3, article S15, 2013.
[182]  Z. Zhang, Y. Zheng, M. Petukh, A. Pegg, Y. Ikeguchi, and E. Alexov, “Enhancing human spermine synthase activity by engineered mutations,” PLoS Computational Biology, vol. 9, no. 2, Article ID e1002924, 2013.
[183]  Z. Zhang, J. Norris, V. Kalscheuer et al., “A Y328C missense mutation in spermine synthase causes a mild form of snyder-robinson syndrome,” Human Molecular Genetics, vol. 22, no. 18, pp. 3789–3797, 2013.
[184]  D. H. Spencer, K. L. Bubb, and M. V. Olson, “Detecting disease-causing mutations in the human genome by haplotype matching,” American Journal of Human Genetics, vol. 79, no. 5, pp. 958–964, 2006.
[185]  B. B. Fitterer, N. A. Antonishyn, P. L. Hall, and D. C. Lehotay, “A polymerase chain reaction-based genotyping assay for detecting a novel sandhoff disease-causing mutation,” Genetic Testing and Molecular Biomarkers, vol. 16, no. 5, pp. 401–405, 2012.
[186]  A. J. P. Smith, J. Palmen, W. Putt, P. J. Talmud, S. E. Humphries, and F. Drenos, “Application of statistical and functional methodologies for the investigation of genetic determinants of coronary heart disease biomarkers: lipoprotein lipase genotype and plasma triglycerides as an exemplar,” Human Molecular Genetics, vol. 19, no. 20, Article ID ddq308, pp. 3936–3947, 2010.
[187]  S. D. Ramsey, D. Veenstra, S. R. Tunis, L. Garrison, J. J. Crowley, and L. H. Baker, “How comparative effectiveness research can help advance “personalized medicine” in cancer treatment,” Health Affairs, vol. 30, no. 12, pp. 2259–2268, 2011.
[188]  C. A. Chapleau, J. Lane, J. Larimore, W. Li, L. Pozzo-Miller, and A. K. Percy, “Recent progress in Rett syndrome and MECP2 dysfunction: assessment of potential treatment options,” Future Neurology, vol. 8, no. 1, pp. 21–28, 2013.
[189]  A. Banerjee, E. Romero-Lorenzo, and M. Sur, “MeCP2: making sense of missense in Rett syndrome,” Cell Research, vol. 23, pp. 1244–1246, 2013.
[190]  K. N. McFarland, M. N. Huizenga, S. B. Darnell, et al., “MeCP2: a novel Huntingtin interactor,” Human Molecular Genetics, vol. 23, no. 4, pp. 1036–1044, 2014.
[191]  B. Suter, D. Treadwell-Deering, H. Y. Zoghbi, D. G. Glaze, and J. L. Neul, “Brief report: MECP2 mutations in people without rett syndrome,” Journal of Autism and Developmental Disorders, vol. 44, no. 3, pp. 703–711, 2014.
[192]  R. Bowser, “Race as a proxy for drug response: the dangers and challenges of ethnic drugs,” De Paul Law Review, vol. 53, no. 3, pp. 1111–1126, 2004.
[193]  S. L. Chan, C. Suo, S. C. Lee, B. C. Goh, K. S. Chia, and Y. Y. Teo, “Translational aspects of genetic factors in the prediction of drug response variability: a case study of warfarin pharmacogenomics in a multi-ethnic cohort from Asia,” Pharmacogenomics Journal, vol. 12, no. 4, pp. 312–318, 2012.
[194]  D. E. Johnson, K. Park, and D. A. Smith, “Ethnic variation in drug response: Implications for the development and regulation of drugs,” Current Opinion in Drug Discovery and Development, vol. 11, no. 1, pp. 29–31, 2008.
[195]  J. M. Gorman, “Gender differences in depression and response to psychotropic medication,” Gender Medicine, vol. 3, no. 2, pp. 93–109, 2006.
[196]  S. Bano, S. Akhter, and M. I. Afridi, “Gender based response to fluoxetine hydrochloride medication in endogenous depression,” Journal of the College of Physicians and Surgeons Pakistan, vol. 14, no. 3, pp. 161–165, 2004.
[197]  A. R. Ferrari, R. Guerrini, G. Gatti, M. G. Alessandrì, P. Bonanni, and E. Perucca, “Influence of dosage, age, and co-medication on plasma topiramate concentrations in children and adults with severe epilepsy and preliminary observations on correlations with clinical response,” Therapeutic Drug Monitoring, vol. 25, no. 6, pp. 700–708, 2003.
[198]  T. Q. Tran, C. Z. Grimes, D. Lai, C. L. Troisi, and L. Y. Hwang, “Effect of age and frequency of injections on immune response to hepatitis B vaccination in drug users,” Vaccine, vol. 30, no. 2, pp. 342–349, 2012.
[199]  V. Y. Martiny and M. A. Miteva, “Advances in molecular modeling of human cytochrome P450 polymorphism,” Journal of Molecular Biology, vol. 425, pp. 3978–3992, 2013.
[200]  M. E. Stauble, A. W. Moore, and L. J. Langman, “Hydrocodone in postoperative personalized pain management: pro-drug or drug?” Clinica Chimica Acta, vol. 429, pp. 26–29, 2014.
[201]  K. Handa, I. Nakagome, N. Yamaotsu, H. Gouda, and S. Hirono, “In silico study on the inhibitory interaction of drugs with wild-type CYP2D6.1 and the natural variant CYP2D6.17,” Drug Metabolism and Pharmacokinetics, vol. 29, no. 1, pp. 52–60, 2014.
[202]  B. Moy, D. Tu, J. L. Pater et al., “Clinical outcomes of ethnic minority women in MA.17: a trial of letrozole after 5 years of tamoxifen in postmenopausal women with early stage breast cancer,” Annals of Oncology, vol. 17, no. 11, pp. 1637–1643, 2006.
[203]  M. Zhan, J. A. Flaws, L. Gallicchio, K. Tkaczuk, L. M. Lewis, and R. Royak-Schaler, “Profiles of tamoxifen-related side effects by race and smoking status in women with breast cancer,” Cancer Detection and Prevention, vol. 31, no. 5, pp. 384–390, 2007.
[204]  A. N. Tucker, K. A. Tkaczuk, L. M. Lewis, D. Tomic, C. K. Lim, and J. A. Flaws, “Polymorphisms in cytochrome P4503A5 (CYP3A5) may be associated with race and tumor characteristics, but not metabolism and side effects of tamoxifen in breast cancer patients,” Cancer Letters, vol. 217, no. 1, pp. 61–72, 2005.
[205]  P. C. Ng, S. S. Murray, S. Levy, and J. C. Venter, “An agenda for personalized medicine,” Nature, vol. 461, no. 7265, pp. 724–726, 2009.
[206]  Y. Bromberg, “Building a genome analysis pipeline to predict disease risk and prevent disease,” Journal of Molecular Biology, vol. 425, no. 21, pp. 3993–4005, 2013.
[207]  J. D. Momper and J. A. Wagner, “Therapeutic drug monitoring as a component of personalized medicine: applications in pediatric drug development,” Clinical Pharmacology & Therapeutics, vol. 95, pp. 138–140, 2014.
[208]  S. J. Bielinski, J. E. Olson, J. Pathak, R. M. Weinshilboum, and L. Wang, “Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time-using genomic data to individualize treatment protocol,” Mayo Clinic Proceedings, vol. 89, pp. 25–33, 2014.
[209]  W. Burke, S. Brown Trinidad, and N. A. Press, “Essential elements of personalized medicine,” Urologic Oncology, vol. 32, no. 2, pp. 193–197, 2014.
[210]  F. R. Vogenberg, C. I. Barash, and M. Pursel, “Personalized medicine: part 2: ethical, legal, and regulatory issues,” Pharmacy and Therapeutics, vol. 35, pp. 624–642, 2010.
[211]  L. S. Welch, K. Ringen, J. Dement et al., “Beryllium disease among construction trade workers at department of energy nuclear sites,” American Journal of Industrial Medicine, vol. 56, no. 10, pp. 1125–1136, 2013.
[212]  A. Kricker, B. K. Armstrong, A. J. McMichael, S. Madronich, and F. de Gruijl, “Skin cancer and ultraviolet,” Nature, vol. 368, no. 6472, p. 594, 1994.
[213]  E. R. Park, J. M. Streck, I. F. Gareen, et al., “A qualitative study of lung cancer risk perceptions and smoking beliefs among national lung screening trial participants,” Nicotine & Tobacco Research, vol. 16, pp. 166–173, 2014.
[214]  B. S. McEwen and L. Getz, “Lifetime experiences, the brain and personalized medicine: an integrative perspective,” Metabolism, vol. 62, supplement 1, pp. S20–S26, 2013.
[215]  K. A. Mussatto, R. G. Hoffmann, G. M. Hoffman, J. S. Tweddell, and L. Bear, “Risk and prevalence of developmental delay in young children with congenital heart disease,” Pediatrics, vol. 133, pp. e570–e577, 2014.
[216]  A. R. Miller, “Lifetime care for patients with autism,” CMAJ, vol. 182, no. 10, pp. 1079–1080, 2010.
[217]  J. van der Leeuw, P. M. Ridker, Y. van der Graaf, and F. L. Visseren, “Personalized cardiovascular disease prevention by applying individualized prediction of treatment effects,” European Heart Journal, vol. 35, no. 13, pp. 837–843, 2014.
[218]  E. Faulkner, L. Annemans, L. Garrison et al., “Challenges in the development and reimbursement of personalized medicine-payer and manufacturer perspectives and implications for health economics and outcomes research: a report of the ISPOR personalized medicine special interest group,” Value in Health, vol. 15, no. 8, pp. 1162–1171, 2012.
[219]  L. Clarke, X. Zheng-Bradley, R. Smith et al., “The 1000 genomes project: data management and community access,” Nature Methods, vol. 9, no. 5, pp. 459–462, 2012.
[220]  G. R. Abecasis, D. Altshuler, A. Auton, L. D. Brooks, and R. M. Durbin, “A map of human genome variation from population-scale sequencing,” Nature, vol. 467, pp. 1061–1073, 2010.
[221]  T. A. de Beer, R. A. Laskowski, S. L. Parks, et al., “Amino acid changes in disease-associated variants differ radically from variants observed in the 1000 genomes project dataset,” PLOS Computational Biology, vol. 9, no. 12, Article ID e1003382, 2013.

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